Dear FSL members
We're trying to use resting-state fMRI for single-subject mapping. We're discussing clean ways to solve the problem of IC splitting. For one particular network (sensory-motor) we tend to consistently get 2-3 ICs when using the built-in dimensionality estimation from FSL. We can solve this by lowering dimensionality or simply adding the ICs together, but this is a bit too subjective. We're considering doing a "dual_regression" without group maps:
In short, we run ICA, we perform a template-matching procedure, we select the best-fit candidate (so, one of the "pieces" of the network), we extract its timecourse and run fsl_glm on the filtered_func data, with the timecourse as design and the --demean option. This is similar to a SBCA analysis, but the ROI is selected based on the initial ICA. From visual inspection, the output masks show the whole-network (and some noise added), but we were wondering if we're missing something obvious. This is what the dual_regression script does, correct?
Also, we output the results as z values with the --out_z option. Can we perform mixture-modelling on these images to get a consistent thresholding value across subjects (eg. 50% false-positive rate?). If so, would the following command (as seen on the MELODIC user guide) do the trick?
melodic -i myZstat --ICs=myZstat --mix=grot.txt -o myZstatMM --Oall --report -v --mmthresh=0.5
Thanks in advance!
Paulo
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